Evidence map›Paper›PMID 34770292›Full record

ArticleSensors (Basel, Switzerland)2021

Mobile 5P-Medicine Approach for Cardiovascular Patients.

Ivan Miguel Pires, Hanna Vitaliyivna Denysyuk, María Vanessa Villasana, Juliana Sá, Petre Lameski, Ivan Chorbev, Eftim Zdravevski, Vladimir Trajkovik, José Francisco Morgado, Nuno M Garcia

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Ivan Miguel PiresInstituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal.ORCID 0000-0002-3394-6762
Hanna Vitaliyivna DenysyukInstituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal.
María Vanessa VillasanaCentro Hospitalar do Baixo Vouga, 3810-164 Aveiro, Portugal.ORCID 0000-0002-4957-9477
Juliana SáFaculty of Health Sciences, Universidade da Beira Interior, 6200-506 Covilhã, Portugal.ORCID 0000-0002-6776-7789
Petre LameskiFaculty of Computer Science and Engineering, SS. Cyril and Methodius University, 1000 Skopje, North Macedonia.ORCID 0000-0002-5336-1796
Ivan ChorbevFaculty of Computer Science and Engineering, SS. Cyril and Methodius University, 1000 Skopje, North Macedonia.ORCID 0000-0002-9648-4438
Eftim ZdravevskiFaculty of Computer Science and Engineering, SS. Cyril and Methodius University, 1000 Skopje, North Macedonia.ORCID 0000-0001-7664-0168
Vladimir TrajkovikFaculty of Computer Science and Engineering, SS. Cyril and Methodius University, 1000 Skopje, North Macedonia.ORCID 0000-0001-8103-8059
José Francisco MorgadoComputer Science Department, Polytechnic Institute of Viseu, 3504-510 Viseu, Portugal.
Nuno M GarciaInstituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal.ORCID 0000-0002-3195-3168

Funding

Fundação para a Ciência e Tecnologia UIDB/50008/2020
6 · The paper itself

Abstract

Medicine is heading towards personalized care based on individual situations and conditions. With smartphones and increasingly miniaturized wearable devices, the sensors available on these devices can perform long-term continuous monitoring of several user health-related parameters, making them a powerful tool for a new medicine approach for these patients. Our proposed system, described in this article, aims to develop innovative solutions based on artificial intelligence techniques to empower patients with cardiovascular disease. These solutions will realize a novel 5P (Predictive, Preventive, Participatory, Personalized, and Precision) medicine approach by providing patients with personalized plans for treatment and increasing their ability for self-monitoring. Such capabilities will be derived by learning algorithms from physiological data and behavioral information, collected using wearables and smart devices worn by patients with health conditions. Further, developing an innovative system of smart algorithms will also focus on providing monitoring techniques, predicting extreme events, generating alarms with varying health parameters, and offering opportunities to maintain active engagement of patients in the healthcare process by promoting the adoption of healthy behaviors and well-being outcomes. The multiple features of this future system will increase the quality of life for cardiovascular diseases patients and provide seamless contact with a healthcare professional.

Indexed as

Artificial IntelligenceWearable Electronic DevicesDelivery of Health CareHumansQuality of LifeSmartphone5P-Medicineartificial intelligencecardiovascular diseasesdigital healthmobile bio-sensing for medicinepatient empowerment technologies

Identifiers

PMID34770292
PMCPMC8587644

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.